Similarly, the classical modular architecture of task bots, as shown in Figure REF , views multi-turn conversations between a system and a human user as an iterative decision making process, where the system is (the agent of) the organism and the user the environment. The system consists of a pipeline of modules that play different roles in decision making. At each iteration, a natural language understanding (NLU) module identifies the user intent and extracts associated information such as entities and their values from user input. A dialog state tracker (DST) infers the dialog belief state (the internal cognitive model of the dialog system). The belief state is often used to query a task-specific database (DB) to obtain the DB state, such as the number of entities that match the user goal. The dialog state and DB state are then passed to a dialog policy (POL) to select the next system action. A natural language generation (NLG) module converts the action to a natural language response. Like cognitive scientists, dialog researchers also believe that the quality of a task bot depends to a large degree upon the performance of dialog state tracking (or its internal cognitive model), which had been the focus of task-oriented dialog research for many years
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